Systems and methods for predictive heart valve simulation

ABSTRACT

Systems and methods are described herein for predictive heart valve simulation. The systems and methods described herein can include segmenting anatomical region of a heart of a patient from image data characterizing the heart of the patient. Anatomical model data that can include three-dimensional shapes of the anatomical regions of the heart can be generated based on the image data. The anatomical model data can be used to generate anatomical model data. The analytical model data can include a three-dimensional mesh of the anatomical regions of the heart. A deformed analytical model that can be indicative of a deformed position of the anatomical regions of the heart and a deformed position of the surgical object can be generated based on the analytical model data.

CROSS-REFERENCED TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/US2017/055046, filed Oct. 4, 2017, entitled “SYSTEMS AND METHODS FORPREDICTIVE HEART VALVE SIMULATION,” which claims the benefit of U.S.Provisional Patent Application No. 62/403,940, filed on Oct. 4, 2016,entitled “SYSTEMS AND METHODS FOR PREDICTIVE HEART VALVE SIMULATION,”the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present specification generally relates to systems and methods forpredictive heart valve simulation.

BACKGROUND

Transcatheter aortic valve replacement (TAVR) can provide treatment forpatients with severe aortic stenosis, and high-risk patients withvarious comorbidities, who cannot undergo conventional open-heartsurgery. Despite the advantages associated with TAVR, complications suchas, for example, conduction abnormalities, significant residual aorticregurgitation, and cerebrovascular events can still occur. In additionto the complications, life-threatening events can also occur duringTAVR. The events can include coronary obstruction, paravalvular leakage,and thrombosis. Coronary obstruction can occur in either the right orleft coronary artery. In some instances, coronary obstruction can bemore prevalent with balloon expandable bioprostheses.

An average left coronary artery height and mean aortic root diameter canbe used to identify criteria that can provide an indication that thepatient is at risk for coronary obstruction. An average left coronaryartery height and mean aortic root diameter can be approximately 10millimeters (mm) and 28 mm, respectively. The identified criteria caninclude a coronary ostium height (basal leaflet insertion to coronaryostium distance) of less than 12 mm (or 10 mm), a sinus of Valsalva(SOV) diameter of less than 30 mm, a valve leaflet length greater thancoronary height relative to an annulus, and a shallow SOV with bulkycalcification The identified criteria can be used to determine if apatient should undergo coronary protection.

Existing criteria based approaches for providing an indication that apatient is at risk coronary obstruction fail to consider certainanatomic factors (e.g., lesion size and/or location, a sinus width at acoronary ostium, a leaflet's length, etc.). Moreover, existingcriteria's in some instances cannot be individualized to the anatomy andconditions of the patient. Thus, existing criteria's for coronaryobstruction fail to provide a sufficient relationship (e.g., detailedinformation on anatomical factors and their respective interrelationshiprelative to a coronary obstruction), and accuracy for guiding a clinicalprocedure decision making process.

SUMMARY

In an example, a method for predictive heart valve simulation caninclude generating anatomical model data based on image datacharacterizing anatomical regions of a heart of a patient. Theanatomical model data can include three-dimensional shapes of theanatomical regions of the heart. The anatomical model data can be usedby a geometric modeling engine to generate analytical model data. Theanalytical model data can include a three-dimensional mesh of theanatomical regions of the heart. The analytical model can be providedwith a three-dimensional mesh of a surgical object. The analytical modeldata can be used by a numerical analysis engine to generate a deformedanalytical model. The deformed analytical model can be indicative of adeformed position of the anatomical regions of the heart and a deformedposition of the surgical object. The deformed analytical model can beevaluated to provide heart functionality measures for the heart.

In another example, a method for predictive heart valve simulation, caninclude segmenting, with one or more processors, anatomical regions of aheart of a patient from image data characterizing the heart of thepatient. The anatomical regions can include one or more calcificnodules, an aortic root that can include a coronary artery, and anaortic leaflet. The image data of the one or more calcific nodules, theaortic root, and the aortic leaflet can be used by the one or moreprocessors to generate anatomical model data. The anatomical model datacan include three-dimensional shapes of the one or more calcificnodules, the aortic root, and the aortic leaflet. A deformed position ofthe aortic leaflet and the calcific nodule can be simulated by the oneor more processors. A gap size can be quantified by the one or moreprocessors based on the deformed position of the calcific nodule and thecoronary artery of the aortic root.

In an even further example, a method for predictive heart valvesimulation can include segmenting anatomical regions of a heart of apatient from image data characterizing the heart of the patient. Theanatomical regions can include one or more calcific nodules, an aorticroot that can include a coronary artery, and an aortic leaflet. Theimage data of the one or more calcific nodules, the aortic root, and theaortic leaflet can be used by an image processing engine to generateanatomical model data. The anatomical model data can includethree-dimensional shapes of the one or more calcific nodules, the aorticroot, and the aortic leaflet. The anatomical model data can be used by ageometric modeling engine to generate analytical model data. Theanalytical model data can include three-dimensional meshes of the one ormore calcific nodules, the aortic root, and the aortic leaflet. Theanalytical model data can be used by a numerical analysis engine togenerate a deformed analytical model. The deformed analytical model canbe indicative of a deformed position of the calcific nodule and thecoronary artery of the aortic root. A gap size can be determined betweenthe deformed position of the calcific nodule and the coronary artery ofthe aortic root.

In another example, a method for predictive heart valve simulation caninclude receiving image data indicative of a heart of a patient. Theimage data can include a calcific nodule, an aortic root that caninclude a coronary artery, and an aortic leaflet. One or more parameterscan be determined based on the anatomical model data. The one or moremodel parameters can include a thickness t of the calcific nodule. Adeformed position of the aortic leaflet and the calcific nodule can bedetermined by a parametric analysis engine based on the one or moremodel parameters. The parametric analysis engine can be programmed tomodel the aortic leaflet in a fully expanded position. A gap size can bequantified with the parametric analysis engine based on the deformedposition of the calcific nodule and the coronary artery of the aorticroot. The gap size can correspond to a two-dimensional distance betweena nodule point on the deformed position of the calcific nodule and anostium point on the coronary artery of the aortic root.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates an example of a system for predictive heart valvesimulation.

FIG. 2 illustrates an example of a method for predictive heart valvesimulation.

FIG. 3 illustrates exemplary image data.

FIG. 4 illustrates exemplary anatomical model data.

FIGS. 5-9 illustrate exemplary analytical model data.

FIGS. 10-14 illustrate exemplary deformed analytical models.

FIG. 15 illustrates an example of a method for predictive heart valvesimulation.

FIGS. 16-19 illustrate an example of slices of computed tomography (CT).

FIGS. 20-21 illustrate exemplary analytical model data.

FIGS. 22-23 illustrate an example of a parametric analysis engine.

FIG. 24 illustrates an example of a method for delivery of aself-expandable stent to a patient.

FIGS. 25A and 25B illustrate exemplary portions of a method for deliveryof a self-expandable stent to a patient.

FIG. 26 illustrates exemplary self-expandable stents.

FIG. 27 illustrates an exemplary method for predicting and quantifyingparavalvular leakage.

FIGS. 28-30 illustrate exemplary anatomical model data collected duringa patient study.

FIGS. 31-33 illustrate exemplary deformed analytical models collectedduring the patient study.

FIGS. 34-39 illustrate exemplary ordered pairs of gap sizes collectedduring the patient study.

DETAILED DESCRIPTION

Systems and methods are described herein for evaluating anatomic factorsof patients. The anatomic factors can be evaluated according to thesystems and methods described herein based on image data. For example,anatomic parameters such as calcium nodule size and location can be usedto predict coronary obstruction. Moreover, the systems and methodsdescribed herein can be used as a framework to quantify coronaryobstruction prior to a procedure, such as transcatheter aortic valvereplacement (TAVR). It is noted that, while the examples describedherein are with reference to TAVR, the examples described herein shouldnot be construed as limited to only TAVR. The examples described hereincan be used to predict outcomes or risks associated with TranscatheterMitral Valve Replacement (TMVR), or any other existing or yet to bedeveloped transcatheter valve replacement or insertion procedure.Exemplary procedures can include, but not limited to, transcathetervalve replacement or insertion in a pulmonary root, pulmonary veinostium, tricuspid annulus, superior vena cava, or inferior vena cava.

FIG. 1 relates to a system 10 for predictive heart valve simulation. Thesystem 10 can be configured to collect image data characterizing a heartof a patient 20. The system 10 can include an imaging device 100. Theimaging device 100 can be configured to college image data 22 in two orthree dimensions of the patient. The image data 22 can include, but notlimited to, X-ray image data (e.g., X-ray computed tomography (CT)images), magnetic resonance imaging (MRI) image data, or ultrasoundimage data. An imaging device 100, as described herein, can correspondto any modality that can be configured to collect image data 22 of thepatient 20, such as the patient's heart.

The imaging device 100 can further include one or more processors 102for executing machine readable instructions and memory 104 for storingthe machine readable instructions. The one or more processors 102 can becoupled to the memory 104, and configured to retrieve the stored machinereadable instructions at the memory 104. The one or more processors 102can include an integrated circuit, a microchip, a computer, or any othercomputing device capable of executing machine readable instructions. Thememory 104 can include RAM, ROM, a flash memory, a hard drive, or anydevice capable of storing machine readable instructions.

The imaging device 100 can further include a sensor 106. The sensor 106can be configured to collect measurements of the heart of the patient20. The sensor 106 can be coupled to the one or more processors 102, thememory 104, or both. It is noted that the term “sensor,” as used herein,corresponds to a device that can be configured to measure a physicalquantity and convert the measured physical quantity into arepresentative signal, which can be correlated to a measured value ofthe physical quantity. In some examples, the imaging device 100 caninclude an X-ray CT system for collecting X-ray data. Accordingly, thesensor 106 can be an X-ray detector, and can be configured to detectphotons such as, for example, a point detector, a linear detector, or aplanar detector.

In some examples, the imaging device 100 can include a source 108. Thesource 108 can be configured to generate excitation energy that can bedetectable by the sensor 106. The source 108 can be coupled to the oneor more processors 102, the memory 104, or both. In examples where theimaging device 100 includes an X-ray CT system, the source 108 can be anX-ray source. The X-ray can be configured to emit photons along a path.The path can begin at the source 108 and terminate at the sensor 106.The heart of the patient 20 can be located along the path, and thusbetween the source 108 and the sensor 106. A portion of the photons canbe absorbed by the patient 20, while measurements are collected by thesensor 106. Accordingly, the photons received by the sensor 106 can beindicative of the patient 20, e.g., the intensity of the photons can becorrelated to the density of patient's 20 body.

The imaging device 100 can further include an actuation assembly 110.The actuation assembly 110 can be configured to manipulate the patient20, the sensor 106, the source 108, or a combination thereof. Forexample, the actuation assembly 110 can include one or moreservo-mechanisms that can be configured to control an amount of forcerequired for manipulating the patient 20, the sensor 106, the source108, or a combination thereof. In the examples described herein, the oneor more processors 102, the memory 104, or both can be integral with anyor all of the sensor 106, the source 108, and the actuation assembly110. However, it is to be understood that the one or more processors102, the memory 104, or both, can be separate components that can becoupled with one another.

In some examples, the actuation assembly 110 can include a mechanicalactuator, a hydraulic actuator, a pneumatic actuator, an electricalactuator, or a combination thereof. The actuation assembly 110 can becoupled to the one or more processors 102, the memory 104, or both. Theone or more processors 102 can be configured to execute the machinereadable instructions to control the operation of the sensor 106, thesource 108, and the actuation assembly 110. The actuation assembly 110can be configured to cause relative motion of the patient 20 withrespect to the sensor 106 and the source 108. For example, the actuationassembly 110 can include a gantry system for moving the sensor 106 andthe source 108 in a substantially circular pattern relative the patient20.

In examples where the imaging device 100 includes an X-ray CT system,multiple measurements of the patient 20 can be collected by the sensor106, relative motion between the patient 20 and the sensor 106, thesource 108, or both. Each measurement can be constructed into an imagehaving greater dimensional complexity than the measurement generated bythe sensor 106. For example, each measurement can be indicative ofabsorption or density of the patient 20, and can be constructed into theimage data 22 indicative of the anatomy of the patient 20. For example,measurements collected by a line detector can be used to produce atwo-dimensional images showing a slice of the patient's anatomy. Aplurality of slices can be combined to provide a full representation ofthe patient 20 in three-dimensions such as, for example, by combiningslices collected along a direction orthogonal to the plane of theslices. Measurements collected by a planar detector can be combined intothree-dimensional images of the patient 20.

The imaging device 100 can further include network interface hardware112. The network interface hardware can be coupled to the one or moreprocessors 102 such that the imaging device 100 can be coupled toanother device via a network. The network can include, but not limitedto, a wide area network (WAN), a local area network (LAN), a personalarea network (PAN), or a combination thereof. The network interfacehardware 112 can be configured to communicate (e.g., send and/or receivedata signals) via any wired or wireless communication protocol. Forexample, the network interface hardware 112 can include an antenna, amodem, LAN port, wireless fidelity (Wi-Fi) card, WiMax card, near-fieldcommunication hardware, or the like. Accordingly, the imaging device 100can be coupled to a network via wires, a WAN, a LAN, a PAN, or the like.

Suitable LANs can include, but not limited to, wired Ethernet and/orwireless technologies such as, for example, Wi-Fi. Suitable PANs caninclude, but not limited to, wireless technologies such as, for example,infrared data association (IrDA), BLUETOOTH, wireless universal serialbus (USB), Z-WAVE, ZIGBEE, or the like. Alternatively or additionally,suitable PANs can further include, but not limited to, wired computerbuses such as, for example, USB and FIREWIRE. Thus, any components ofthe imaging device 100 can utilize one or more network components tocommunicate data via the network.

The system 10 can further include an image analysis device 200. Theimage analysis device can be configured to executing machine readableinstructions to provide image analysis and anatomical simulationfunctionality based on anatomical information extracted from the imagedata 22. The image analysis device 200 can include one or moreprocessors 202. The one or more processors 202 can be configured toretrieve and execute the machine readable instruction stored in memory204. The one or more processors 202 can be coupled to network interfacehardware 206. It is noted that, while the image analysis device 200 isillustrated in the example of FIG. 1 as being a single machine, each ofthe one or more processors 202, the memory 204, and the networkinterface hardware 206, including their components and functions, can bedistributed amongst a plurality of machines that can be communicativelycoupled to one another. Additionally, it is noted that in some examples,the image analysis device 200 and the imaging device 100 can beimplemented on a single machine. The image analysis device 200 canfurther include a display 208. The display 208 can be coupled to the oneor more processors 202. Alternatively or additionally, the display canbe provided as a wearable device, such as, for example a smart watch ora virtual reality headset. Suitable example of virtual reality headsetscan include Samsung Gear VR, Sony PlayStation VR, Oculus Rift, or thelike.

In view of the foregoing structural and functional features describedabove, a method that can be implemented will be better appreciated withreference to FIGS. 3, 15, and 24. While, for purposes of simplicity ofexplanation, the method of FIGS. 3, 15, and 24 are shown and describedas executing serially, it is to be understood and appreciated that suchmethod is not limited by the illustrated order, as some aspects could,in other examples, occur in different orders and/or concurrently withother aspects from that shown and described herein. Moreover, not allillustrated features may be required to implement a method. The methodor portions thereof can be implemented as instructions stored in one ormore non-transitory storage media as well as be executed by a processingresource (e.g., one or more processor) of a system, for example, theimage analysis device 200.

FIG. 2 illustrates an example of a flow diagram illustrating an examplemethod 120 for predictive heart valve simulation. The method 120 caninclude a process 122 for providing image data 22. In some examples, theimage data 22 can include X-ray CT image data collected based on thepatient 20. The image data 22 can be collected prior to a clinicalprocedure. For example, prior to performing a heart procedure (e.g.,TAVR), the image data 22 can be generated based on the patient 20,which, as described herein, can be used to predict the outcome or risksassociated with the clinical procedure. The image data 22 cancharacterize an anatomical region of the patient 20. The anatomicalregion can include one or more of the pulmonary root, pulmonary veinostium, tricuspid annulus, superior vena cava, or inferior vena cava.

The image data 22 can be stored in the memory 104 of the imaging device104. In some examples, the image data 22 can be organized such as, forexample, into systole data and/or diastole data. The image data 22 canbe transmitted to the memory 204 of the image analysis device 200 suchas, for example, via the network interface hardware 112 and the networkinterface hardware 206. Additionally, the image data 22 can be stored onor transmitted via an intermediary device that can include memory suchas, for example, a cloud storage device or a portable memory.

The method 120 can further include a process 124 for segmenting theimage data 22. At process 124, the image data 22 can be used to generateanatomical model data 24, such as shown in FIG. 4. The anatomical modeldata 24 can include computer-aided design (CAD) shapes composed ofpoints, curves, surfaces, solids, or the like encoded into a machinereadable format. In some examples, the image analysis device 200 canexecute an image processing engine 210 provided on the memory 204. Theimage processing engine 210 can be programmed to generate the anatomicalmodel data 24 based on the image data 22. The anatomical model data 24can be provided in two-dimensions or three-dimensions. For example, CTimages can include pixels or voxels indicative of relative intensitythat can be encoded into a machine readable format such as, for example,Digital Imaging and Communications in Medicine (DICOM) format, X-ray,raw image data, or the like. Accordingly, the image processing engine210 can include image processing methods that can evaluate CT images.Suitable commercial software toolkits including image processing methodsare available such as, but not limited to, RadiAnt™, available fromMedixant, and Mimics available from Materialise.

At process 124, the image processing engine 210 can be furtherprogrammed to segment image data characterizing one or more anatomicalregions 26 from the image data 22. The imaging processing engine 210 canbe programmed to generate the anatomical model data 24 based on thesegmented image data. For example, the anatomical regions 26 can includeanatomy of the patient 10 that can be manipulated during a clinicalprocedure. In the example of TAVR, the anatomical regions 26 can includean aortic root 28, native aortic leaflets 30, and calcific nodules 32.The calcific nodules 32 can correspond to calcium based deposits thatcan develop within the patient 20. The calcific nodules 32 can have anirregular geometric shape and can vary in size and shape for eachpatient 20. The segmentation can make use of various algorithms such as,for example, thresholding, edge detection, shape recognition, filtering,clustering, or the like. For example, the anatomical regions 26 of CTimages can include different ranges of intensity (e.g., pixel or voxel)relative to tissue stiffness. Once segmented, each of the anatomicalregions 26 can be transformed into a CAD shape within the anatomicalmodel data 24.

The method 120 can further include a process 124 for defining analyticalmodel data 34, such as shown in FIGS. 5, 6, 7, 8A, 8B, and 9. Generally,the analytical model data 34 can include mesh elements such as, forexample, nodes and edges, that can be used for numerical analysis. Insome examples, the image analysis device 200 can be configured toexecute a geometric modeling engine 212 provided on the memory 204. Thegeometric modeling engine 212 can be programmed to generate analyticalmodel data 34 based on the anatomical model data 24. Alternatively oradditionally, the analytical model data 34 can include a surgical object36 representative of model implants, surgical instruments, or any otherdevice that can interact with the anatomical regions 26 of the patient20 during the clinical procedure. For example, the surgical objects 36can include a three-dimensional model of a transcatheter aortic valve(TAV) 37, such as shown in FIGS. 8A and 8B. The TAV 37 can include astent 12 that can be configured to extend between a top portion 14 and abottom portion 16 of the TAV 37. The stent 12 can include artificialleaflets 18. Additionally, or alternatively, the TAV 37 can be modeledby a correspondingly shaped cylinder 39. Suitable commercial softwaretoolkits for implementing the geometric modeling engine 212 can include,but not limited to, SolidWorks®.

In the example of TAVR, the analytical model data 34 can include meshes.The meshes can correspond to each of the aortic root 28, the aorticleaflets 30, the calcific nodules 32, and the surgical object 36. Themeshes can be mapped to the CAD shapes of the anatomical regions 26 andthe surgical objects 36. For example, the nodes can be mapped to curves,surfaces, points, or the like of the anatomical model data 24. The nodesand edges of the mesh can be formed in a variety of shapes such as, forexample, triangle, quadrilateral, tetrahedron, pyramid, hexahedron, orthe like. In a test example, 10-node tetrahedral elements were mappedwith a patch-independent algorithm to the anatomical regions 26corresponding to soft tissue regions. The stent 12 was meshed usinghexahedron elements. The total number of mesh elements varied for eachpatient, and was a function of the shape and size of the anatomicalregions 26 (e.g., aortic wall 38, aortic leaflets 30, coronary arteries40, and calcific nodules 32).

The method 120 can further include a process 128 for simulating theclinical procedure. In some examples, the image analysis device 200 canbe configured to execute a numerical analysis engine 214 provided on thememory 204. The numerical analysis engine 214 can be programmed to mapboundary conditions, and a system of equations to the analytical modeldata 34. The numerical analysis engine 214 can be programmed to solvethe system of equations based on the boundary conditions to simulate theclinical procedure. For example, the numerical analysis engine 214 canbe programmed for finite element analysis (FEA), computational fluiddynamics (CFD), or the like. Suitable commercial software toolkits forimplementing the numerical analysis engine 214 can include, but notlimited to, ANSYS® available from ANSYS, Inc.

The numerical analysis engine 214 can be programmed to simulate theclinical procedure by assigning boundary conditions to the analyticalmodel data 34 and manipulating the surgical objects 36 to resemble theclinical procedure. In the example of TAVR, the numerical analysisengine 214 can be programmed to model an impact of the clinicalprocedure upon the anatomy of the patient 20. For example, the movementaortic leaflets 30 and the calcific nodules 32 can be modeled by thenumerical analysis engine 204 to quantify an amount of coronaryobstruction, paravalvular leakage, thrombosis, or a combination thereof.The material properties of the aortic root 28 and the aortic leaflets 30can be considered to be linear elastic, and the calcific nodules 32 andcan be modeled by the numerical analysis engine 204 as rigid objects.The physical characteristics of the aortic root 28 and the aorticleaflets 30 can be mapped to the analytical model data 34, e.g., themesh can be assigned a Young's modulus of about 2,000 kilopascal (kPa),Poisson's ratio of about 0.495, and a density of about 1,000 kilogramsper meter squared (Kg/m³). Moreover, the surgical object 36 can bemodeled by the numerical analysis engine 204 as the stent 12, the TAV37, the cylinder 39, or the like. For example, the surgical object 36can be modeled by the numerical analysis engine 204 as homogeneousisotropic stainless steel with a Young's modulus of about 205 gigapascal(GPa), Poisson ratio of about 0.275, and tensile strength of about 620megapascals (MPa).

A pessimistic scenario can be modeled by considering deformation of theaortic leaflets 30 in a fully expanded position. In some examples, thepessimistic scenario can be simulated by representing the TAV 37 as thecylinder 39 that expands in the analytical model data 34 (e.g., thesurgical object 36 can be a cylinder 39 with expanding dimensions). Itis noted that more complex scenarios can be modeled by representing theTAV 37 with a less idealized model without departing from the examplesdescribed herein. In some examples, the surgical object 36 can include amodel of the TAV 37, which can be expanded in a manner that imitates aphysical deployment (e.g., dimensions, force, rate of change) of the TAV37 (e.g., a self-expanding device or a balloon-expanding device). Thesurgical object 36 can be deployed at a center of the commissures toexpand the aortic leaflets 30. Since a contact coefficient between thestent 12 and aortic leaflets 30 is not well known, a frictionlesscontact can be specified. Alternatively, the contact coefficient can bespecified. In addition, for better convergence, a “Normal Lagrange”formulation and “Adjust to Touch” interface treatment can be used at acontact region. To account for a nonlinearity of the problem, a sparsedirect solver with full Newton-Raphson control can be used. Adisplacement control boundary condition can be applied to the surgicalobject 36 based on an annulus diameter of the aortic root 28.

The numerical analysis engine 214 can be further programmed to generatea deformed analytical model 42 by modeling the impact of changing thedimensions of the surgical object 36. For example, as the dimensions ofthe surgical object 36 change, the position of aortic leaflets 30 andthe calcific nodules 32 can respond by changing position (e.g., theaortic leaflets 30 can expand radially to cause the calcific nodules 32attached thereto to change position). Likewise, the surgical object 36can deform in response to interaction with the aortic leaflets 30 andthe calcific nodules 32. Accordingly, each deformed analytical model 42can correspond to a deformed position of the aortic leaflets 30, thecalcific nodules 32, the surgical object 36 caused by the TAVR. Anynumber of deformed analytical models 42 can be defined to model aninitial deployment 44 of the surgical object 36, such as shown in FIG.10, an intermediate deployment of the surgical object 36, such as shownin FIG. 11, a full deployment 48 of the surgical object 36, such asshown in FIGS. 12A and 12B, and any position there between.

In some examples, the surgical object 36 can be changed in theanalytical model data 34 to generate additional deformed analyticalmodels 42. Accordingly, the numerical analysis engine 214 can beprogrammed to predict the impact of the use of different sizes or typesof TAV's 37 upon the anatomy of the patient 20. Moreover, the surgicalobject 36 can be repositioned in the analytical model data 34 todetermine the impact of changes in positioning upon the deformedanalytical models 42. For example, the TAV 37 can have an insertiondepth 60 (e.g., a distance between the top portion 14 of the TAV 37 andthe annulus 62 of the aortic root 28). Additionally, a pitch angle andyaw angle relative to a centerline 64 of the aortic root 28 can bemodeled by the numerical analysis engine 214. Accordingly, the pitchangle, yaw angle, insertion depth 60, or a combination thereof, can bemodeled to quantify a sensitivity of the patient 20 to the TAVR. In someexamples, deformed analytical models 42 can be generated consecutively,or in parallel, to allow for direct comparison of different sizes,types, or positions of TAV's 37. For example, each of the deformedanalytical models 42 can be depicted on the display 208. Thus, anefficacy of each TAV 37 can be visualized prior to the clinicalprocedure, for example, prior to performing TAVR.

Referring collectively to FIGS. 12A and 12B, after modeling thedeformation of the aortic leaflets 30 caused by full deployment 48 ofthe surgical object 36, a gap size α_(3D) can be determined. The gapsize α_(3D) can correspond to a shortest three-dimensional distancebetween a coronary ostium of the coronary artery 40 and a potentialobstruction such as, for example, a calcific nodule 32 on the aorticleaflets 30, or the aortic leaflets 30. Thus, the gap size α_(3D) can bedetermined based on a position of the aortic leaflets 30 after TAV stentdeployment. The gap size α_(3D) can be correlated to risk of coronaryobstruction. For proper heart function, blood travels over the aorticleaflets 32 to reach the coronary ostium. During TAV stent deployment,aortic leaflets 32 can be forced towards the coronary arteries 40 toaccommodate the new valve prosthesis. A life-threatening complicationknown as coronary ostium obstruction can occur when the aortic leaflets32 are forced into a position that blocks the coronary ostia, cuttingoff blood flow to remaining portions of the heart.

Accordingly, a small gap size α_(3D) (e.g., less than about 3millimeters (mm)) can provide an indication that the coronary artery 40is blocked. Moreover, it is noted that for some patients, the gap sizeα_(3D) can be measured relatively easily. However, for other patients,especially those at high risk for coronary obstruction, additional viewsand inspection can be required to determine the gap size α_(3D).Accordingly, the deformed analytical model 42 can improve an accuracy ofthe diagnosis by providing a full three-dimensional geometricrepresentation of the calcific nodule 32, the aortic leaflets 32, andthe coronary artery 40.

Referring collectively to FIGS. 8A, 8B, 12A and 12B, after modeling thedeformation of the aortic leaflets 30 caused by full deployment 48 ofthe surgical object 36, a gap size can be determined to quantifyparavalvular leakage (e.g., undesired blood flow between the TAV 37 andthe annulus of the aortic root 28). The gap size can correspond to alargest three-dimensional distance between the stent 12 or artificialleaflets 18 relative to the annulus of the aortic root 28. Generally,the gap size can be correlated to risk of paravalvular leakage.

The numerical analysis engine 214 can further be programmed to simulateblood flow 49 properties for any of the deformed analytical models 42,such as shown in FIGS. 13 and 14. For example, the geometry of thedeformed analytical model 42 corresponding to full deployment 48 of thesurgical object 36 can be used for CFD to model blood flow 49 propertiesin aortic root 28 region under different conditions. In some examples,the blood flow 49 can be used to quantify paravalvular leakage. Forexample, the amount and rate of blood flow 49 flowing between the TAV 37and the aortic root 28 can be indicative of the relative risk forparavalvular leakage. Alternatively or additionally, the blood flow 49properties of the deformed analytical model 42 can quantify thrombosis.Thrombosis can correspond to localized coagulation or clogging of theblood induced by the TAVR.

The blood flow 49 can be used to identify the TAVR induced blood flowstasis zones. For example, the results of the blood flow 49 can bedisplayed on the display 208 to illustrate and quantify blood flowstasis zones. Accordingly, the blood flow stasis zones can be indicativeof risk for thrombosis. Additionally, the CFD can be used to model aflow pattern 50 of contrast agent flow in coronary artery 40, which canbe used to validate the numerical analysis engine 214 or the efficacy ofthe modeled clinical procedure with data collected during and/orfollowing the clinical procedure. For example, comparing the arrangementof the calcific nodule 32 arrangement and flow patterns in the CFDrelative to aortographic images captured during and/or after theclinical procedure can provide insight into the accuracy of deformedanalytical model 42 and the CFD.

Referring collectively to FIGS. 1, 3, 4, 15, 16, 17, 18, 19, 20, and 21,the examples provided herein can further include a method 130 forpredictive heart valve simulation. The method 130 can include a process132 for generating parameters indicative of the anatomical regions 26 ofthe patient 20. In some examples, the parameters can be generateddirectly or indirectly based on the image data 22. For example, theimage data 22 can include a plurality of slices of CT data 52representative of the left coronary leaflet 54 and the right coronaryleaflet 56, such as shown in FIGS. 16, 17, 18, and 19. The CT data 52can be directly measured for determining parameters for the anatomicalregions 26. The parameters can include, for example, a coronary ostiumheight relative to the annulus baseline, an annulus diameter, and/or asinotubular junction (STJ) diameter, which can correspond to the finalposition of the coronary leaflets 30 after deployment.

Alternatively or additionally, model parameters can be determined basedon the plurality of slices of CT data 52 of the left coronary leaflet 54and the right coronary leaflet 56. The model parameters 58 can include aheight h of coronary artery 40 from the annulus, a thickness t of thecalcific nodule 32 on the left coronary leaflet 54, a thickness t of thecalcific nodule 32 on the right coronary leaflet 58, a projection ofcoronary ostium diameter d on the annulus to STJ line, a sinus width wbetween coronary ostium and the annulus to STJ, a leaflet length l ofthe left coronary leaflet 54, and a leaflet length l of the rightcoronary leaflet 56. Since the aortic leaflets 30 undergo the moststrain during diastole, the image data 22 can be captured in a diastolicphase of a cardiac cycle. In further examples, the parameters, the modelparameters, or both can be generated based on the anatomical model data24.

Referring collectively to FIGS. 1, 15, 22, and 23, the method 130 canfurther include a process 134 for simulating the clinical procedure. Insome examples, the image analysis device 200 can be configured toexecute a parametric analysis engine 216 provided on the memory 204. Theparametric analysis engine 216 can be programmed to simulate the impactof the clinical procedure upon the size and the location of the calciumnodule 32 based on the model parameters. When the parametric analysisengine 216 simulates the TAVR, a gap size α_(2D) can be determined bymodeling the coronary leaflets 30 in a fully expanded position (e.g.,such as shown in FIG. 21) due to TAV stent deployment. The gap sizeα_(2D) can correspond to a two-dimensional distance between the tip ofthe coronary leaflet 30 and coronary ostium of the coronary artery 40.Generally, the gap size α_(2D) can be correlated to risk of coronaryobstruction. It is noted that the parametric analysis engine 216 can beprogrammed to model anatomy of the patient 20 in two-dimensions todetermine the gap size α_(2D).

The parametric analysis engine 216 can be further programmed todetermine a location of two points: nodule point P_(C), which cancorrespond to the position of the calcific nodule 32 of the aorticleaflet 30; and ostium point P_(O), which can correspond to the positionof the upper edge of the coronary ostium of the coronary artery 40.Accordingly, the gap size α_(2D) can be calculated by the parametricanalysis engine 216 based on:

α_(2D)=√{square root over ((Δx)²+(Δy)²)}  (Equation 1),

wherein Δx is a horizontal offset (x-direction) between the nodule pointP_(C) and the ostium point P_(O), and Δy is a vertical offset(y-direction) between the nodule point between P_(C) and the ostiumpoint P_(O).

The horizontal offset Δx can be determined based on Equation 2 and thevertical offset Δy can be determined based on Equation 3:

Δx=w−t  (Equation 2),

Δy=h+d−l  (Equation 3),

wherein the following model parameters can be used: the sinus width w atthe ostium level of the coronary artery 40, the thickness t of thecalcific nodule 32 on the tip of the aortic leaflet 30, the leafletlength l, height h of the coronary ostium of the coronary artery 40, andcoronary ostium diameter d of the coronary artery 40.

The parametric analysis engine 216 can be further programmed tocalculate the gap size α_(2D) for both left and right coronary ostium ofthe coronary arteries 40.

FIG. 24 illustrates an example of a method 2400 for delivery of aself-expandable stent to a patient. The self-expandable stent (or“stent”) can correspond to any stent described herein, available, or canbecome available. In an example, the stent can correspond to a stent,such as shown in FIG. 26. The method 2400 can begin at step 2402,wherein models of patient-specific geometry can be generated and alignedwith one or more objects. FIG. 25A illustrates a more detailed view ofthe step 2402, as shown in FIG. 24. In some examples, the models caninclude CAD models. The patient specific geometry can include an aorticwall, leaflets, and calcium nodules. The patient specific geometry canbe aligned with a catheter (e.g., a cylinder with a given diameter basedon a type of stent, e.g., valve type). The patient specific geometry canfurther be aligned with a crimper (e.g., a funnel with a diametersubstantially equal to the diameter of the catheter, and with a greaterdiameter than an in-flow diameter of the stent). The patient specificgeometry can further be aligned with the self-expandable stent (e.g., aTAV stent).

At 2404, a crimper can be employed to gradually crimp the stent. At2406, the crimper simultaneously with the catheter can be configured tomove toward the self-expandable stent (e.g., displacement boundarycondition in an axial direction) such that bottom nodes of the TAV stentand the catheter are in a similar plane. The bottom nodes of the TAVstent can be fixed in a radial direction and free in other directions(e.g., axial and circumferential direction).

At 2408, the catheter along with the crimped TAV stent can be implantedat an aortic site (e.g., an aortic root) while the crimped TAV stent canbe located inside the catheter. A particular location of a valve differsin patients, and can depend on anatomical factors of the patientspecific geometry At 2410, while bottom nodes of the TAV stent are fixedin the radial direction and free in the other directions (e.g., axialand circumferential directions), the catheter can be configured torelease the TAV stent gradually (e.g., the displacement boundarycondition in the axial direction). At 2412, the catheter can be removed,and the TAV stent can be in the fully expanded configuration at theaortic site. FIG. 25B illustrates a more detailed view of the step 2412,as shown in FIG. 24.

After deployment the TAV stent outcomes of the clinical procedure can beevaluated. A final position of the native leaflets and calcium nodulesrelative to coronary arteries can be presented based on proper slices.The final configuration of the TAV can be analyzed. All the stressdistributions on either the patient-specific geometry or TAV can bemeasured for further evaluations according to the systems and methodsdescribed herein. The material that can be used for the TAV stent caninclude Nitinol. The material properties of the patient-specificgeometry can be modeled according to a hyper-elastic model. Calciumnodules can be modeled according to a linear-elastic model. Both crimperand catheter can be modeled as a rigid models.

In an example of an aortic valve replacement, the TAV stent can bepositioned relative to the aortic site such that risks associated with aTAVR procedure can be substantially mitigated based on the systems andmethods described herein. Such risks can include, but not limited to,coronary obstruction, paravalvular leakage, and thrombosis. Based on thesystems and methods described herein, the stent can be positionedrelative to the aortic site such that the stent can be deployed at theaortic site with zero to minimal resulting complications. Thus, thesystems and methods described herein can substantially improve anaccuracy and quality of a TAVR procedure, and thereby substantiallyreduces the risks associated with the procedure. Accordingly, thesystems and methods described herein can be used a framework to quantifya risk (e.g., coronary obstruction) associated with the TAVR procedureprior to the procedure.

The quantified risk can be used to control the subsequent TAVRprocedure. The systems and methods described herein can be used topredict risks associated with the TAVR procedure, and can be used tocontrol the TAVR procedure such that the risks associated with theprocedure are substantially mitigated. Controlling the TAVR procedurecan include controlling one or more parameters of the TAVR procedure.The one or more parameters can include an orientation of the stentrelative to the aortic site, a valve type and size, prior coronaryprotection, paravalvular leak consideration, and a need for the TAVRprocedure.

FIG. 26 illustrates exemplary stents 2600 according to the systems andmethods described herein. The exemplary stents 2600 can include aplurality of stents that can have varying diameters. Alternatively, theexemplary stents can include a plurality of stents that can havesubstantially similar diameters. The exemplary stents 2600 can include aplurality of self-expandable stents 2602, 2604, and 2606, and a balloonexpandable stent 2608.

FIG. 27 illustrates an exemplary method 2700 for predicting andquantifying paravalvular leakage. The method 2400 can begin at step2702, wherein after deployment of a TAV stent inside a patient-specificgeometry, a final configuration of the TAV stent and thepatient-specific geometry can be used as an initial geometry for CFDsimulations. At 2704, potential gaps between the TAV stent and an innerwall of the patient-specific geometry can be identified for paravalvularleakage by applying a simulated blood flow from the ascending aortarelative to a left ventricle of the heart. At 2706, a section at anascending aorta (e.g., top surface) can be defined as a flow inlet, anda section at the left ventricle can be defined as a flow outlet. At2708, a maximum pressure gradient between the left ventricle andascending aorta can be applied at the inlet. The outlet pressure can beset to zero such that the gradient can cause the fluid to flow from theinlet to the outlet. Since leakage flow is being studied, the flow canbe in a reverse direction compared to a flow exiting the aortic valve.At 2710, after obtaining the steady state solution, locations andquantities of leakage flows can be measured based on jet velocity.

EXAMPLES

The examples provided herein were evaluated based on CT images of nine(9) patients who underwent TAVR. Three of the patients experiencedcoronary obstruction. Each of the patients were evaluated based on CTimages acquired prior to TAVR. Using a parametric analysis engine (e.g.,the parametric analysis engine 216, such as shown in FIG. 1), the gapsize α_(2D) was calculated for both the left and right coronary arteriesof the nine patients. The values as well as clinical statuses of thenine patients studied are summarized below in Table 1.

TABLE 1 Measurements from Patients α_(2D) for α_(2D) for α_(3D) forα_(3D) for Left Right Left Right Coronary Coronary Coronary CoronaryCoronary TAVR Coronary Patients Ostium Ostium Ostium Ostium ObstructionOperation Obstruction # (mm) (mm) (mm) (mm) Risk Level CompletedConfirmation A 14.78 7.52 12.38 7.4 low Yes No B 3.53 3.62 2.58 3.39 lowYes No C 8.69 5.96 6.68 7.07 low Yes No D 3.87 2.26 4.6 2.54 moderateYes No E 0.98 5.69 0.93 6.4 high No n/a F 2.16 2.24 0.85 3.13 high Non/a G 0.60 4.24 0.7 7.46 high Yes Yes H 7.50 6.85 5.99 6.33 low Yes YesI 0 0 0 0 high Yes Yes

After evaluating the gap size α_(2D) values for the nine patients, thenine patients were categorized into three groups: low risk, moderaterisk, and high risk of coronary obstruction for either coronary ostia.Four of the patients were categorized as low risk, one patient wascategorized as moderate risk, and four of the patients were categorizedas high risk. The TAVR status and the occurrence of coronary obstructionis also shown in Table 1. Of the four patients who were placed in thehigh risk category, two patients underwent TAVR (Patient G and PatientI). Patient G and Patient I were confirmed to experience coronaryobstruction. For Patient G, the coronary obstruction proved fatal.Patient I was successfully rescued via open heart intervention.

Patient H was characterized as low risk. While patient H did experiencecoronary obstruction, the coronary obstruction was due to blockage fromprosthetic leaflet subannular membrane material, and not blockage fromthe native leaflets. Patient H was successfully rescued via open heartintervention. For the other two high risk patients, Patient E declinedany surgical intervention because of the high risk, and Patient F wasadmitted for open heart surgery. The remaining moderate and low riskpatients successfully underwent TAVR without coronary obstruction.

Referring collectively to FIGS. 28, 29, and 30, anatomical model datawas reconstructed for each of the patients. Aortic and ventricular viewsof the anatomical model data for each patient's reconstructed aorticroot geometry are provided. Aortic views are oriented with thecommissure of non and left coronary cusps on top. In the ventricularviews, however, the top commissure corresponds to the left and rightcoronary cusps. Calcific nodules (colored yellow) were reconstructedseparately from the aortic root (colored red) and then added to theleaflets. The geometry for Patient H, who has a failed bioprostheticsurgical valve implanted is colored in grey. Although basiccharacteristics of all the patients such as tri-leaflet valves, twocoronary arteries, and arrangement of the cusps are similar, eachpatient has a unique aortic geometry with different patterns andseverity of calcification (e.g., different size, shape, and position ofcalcific nodules).

Referring collectively to FIGS. 31, 32, and 33, analytical model datawas generated based on the anatomical model data. The numerical analysisengine was used to determine deformed analytical models based on theanalytical model data. The deformed analytical models corresponding toTAV stent deployment were extracted. Cross-sectional views of both leftand right coronary arteries were selected from the three-dimensionalgeometry to show the final position of leaflets relative to the left andright coronary ostia. These cross-sections include the ostium centerlineas well as maximum calcification thickness on the leaflet tip.Cross-sectional views of simulated post-deployment anatomy of the ninepatients for both left and right coronary ostium are provided. For easeof recognition, the edge of the leaflets are highlighted in red, andcalcific nodules on the leaflets are highlighted in yellow. For patientspreviously determined the parametric analysis engine as being high riskfor coronary obstruction, the three-dimensional cross-sectional viewsalso illustrate the possibility of the native leaflets blocking theostia.

The gap size α_(3D) for each of left and right coronary ostia wasmeasured based on the deformed analytical models. The gap size α_(3D)for each of the patient is summarized above in Table 1. Based on the gapsize α_(3D), the patients were again categorized as low risk, moderaterisk, or high risk for coronary obstruction. The categorization basedupon the gap size α_(3D) agreed well with the categorization based uponthe gap size α_(2D). Patients A, B, C, and H were categorized as lowrisk for coronary ostia obstruction due to stent deployment, Patient Dwas categorized with potential obstruction of the right coronary ostia,and Patients E, F, G, and I were categorized as having high risk of leftcoronary ostium obstruction.

Referring collectively to FIGS. 34, 35, 36, 37, 38, and 39, afterdetermining gap size α_(2D) based on model parameters obtained from CTimages, and gap size α_(3D) using the numerical analysis engine, gapsize α_(2D) data were plotted against gap size α_(3D) data for both leftand right coronary arteries of each patient. The α_(2D)=α_(3D)regression line is depicted in FIGS. 34, 35, 36, 37, 38, and 39 toprovide reference for perfectly matched data. The R² value, which is astatistical parameter indicating closeness of data points to the fittedregression line, is also depicted. Red data points indicate patientswith high-risk coronary obstruction, and blue points show patients withlow-risk coronary obstruction. The results depicted in FIGS. 34 and 35were determined by neglecting calcification thicknesses for thecalculation of gap size α_(2D). The R² value for the left coronaryartery was 0.55 and the R² value for the right coronary artery was 0.46.The gap size α_(2D) data depicted in FIGS. 36 and 37 were determinedconsidering the leaflet tip calcific nodule thickness. The R² value forthe left coronary artery was 0.92 and the R² value for the rightcoronary artery was 0.73. Thus, after including the calcific nodule sizeeffect, a significant improvement was observed in the R² values. Any ofthe gap sizes provided herein can include a normalized gap size that isnormalized according to an anatomical distance. For example, the gapsize α_(2D) data and gap size α_(3D) data depicted in FIGS. 36 and 37were normalized according to the respective diameter of the leftcoronary artery and right coronary artery. The normalized data isdepicted in FIGS. 38 and 39. The normalized data showed furtherimprovement of the R² values. The R² value for the left coronary arterywas 0.92 and the R² value for the right coronary artery was 0.86.Likewise the gap size for paravalvular leakage can be normalized for byan anatomical distance of the patient.

According the examples described herein, calcification thickness on theleaflet tip can be used to construct a normalized cut-off factor toevaluate risk of coronary obstruction prior to TAVR. As noted above,neglecting calcium nodule thickness in the calculation of the gap sizeα_(2D), e.g., based only on coronary height, leaflet length, and sinuswidth at the coronary ostium, can lead to overestimation of the gap sizeα_(2D) for patients with high risk, under predicting the level of riskfor coronary obstruction. Additionally, the comparison of the gap sizeα_(3D) and the gap size α_(2D) showed relatively weak correlations(e.g., R² value for the left coronary artery was 0.55 and the R² valuefor the right coronary artery was 0.46). Considering calcific nodulethickness in the calculation of the gap size α_(2D) can improve thecorrelation with the gap size α_(3D), e.g., the R² value for the leftcoronary artery was 0.92 and the R² value for the right coronary arterywas 0.73.

To further improve the correlation, normalized equivalent parameterswere determined for both the gap size α_(2D) and the gap size α_(3D) bynormalizing the gap size α_(2D) and the gap size α_(3D) with respect totheir corresponding coronary artery diameter. Consequently,normalization led to a clear cut-off ratio of 0.50 for patients withconfirmed or high risk coronary obstruction. This ratio provides anindication that coronary obstruction is likely probable when the finaldistance between the native leaflets and ostium, e.g., the gap sizeα_(2D) or the gap size α_(3D), is less than about half of thecorresponding coronary artery diameter.

It should now be understood that the examples described herein relate tosystems and methods for quantifying a prediction of coronary obstructionin patients with severe aortic stenosis prior to TAVR. For example,model parameters including the position and location of calcific nodulescan be collected and provided to a parametric analysis engine to predictan amount of coronary blockage that can result from the TAVR.Alternatively or additionally, analytical model data can be generatedbased on the three dimensional geometry of the patients anatomy. Anumerical analysis engine can analyze the analytical model data togenerate deformed analytical models. Accordingly, the amount of coronaryblockage resulting from the TAVR can be quantified according to patientspecific morphologies of the aortic root.

Moreover, the systems and methods described herein can be used toevaluate patient geometrical factors prior to TAV implantation based onCT image data. For example, various types and sizes of valves can beevaluated in order to identify a valve and diameter size that is bestsuited for the patient. In addition to the valve itself, the evaluationscan prevent complications such as coronary artery ostium obstruction.Despite the life-threatening nature of coronary artery ostiumobstruction, existing valve manufacturers have no specific safetyguidelines in place to minimize the chance of coronary ostiumobstruction. Moreover, manufacturer guidelines are often neglected bysurgeons who have successfully performed operations outside of theguidelines.

Further advantages of the systems and methods described herein includeproviding a more accurate cut-off factor that is more suited to preventcoronary ostium obstruction. For example, while some studies haveidentified contributing factors such as coronary height, SOV diameter,and leaflet lengths, the studies have failed to consider the effect ofcalcific nodule size and location. The systems and methods describedherein can be used to quantify an impact of calcific nodules on theamount of coronary ostium obstruction (e.g., gap sizes or normalized gapsizes) expected to be experienced due to TAVR.

The systems and method described herein can make use ofthree-dimensional anatomical model data to improve an accuracy andconsistency of collecting parameter information. For example, CT imagedata of an aortic root geometry can include a series of slices, each ofwhich can represent specific cross-sections of the patient's anatomy.The accuracy of a measured parameter can be a function of the sliceselected for measurement. Since slice selection is use-defined,technicians can introduce bias (e.g., errors) when measuring parameters.The three-dimensional anatomical model data can substantially mitigatetechnician bias. For example, cross-sections can be generated from anyportion of the data, and not just the native image orientation.Accordingly, the most severe aspects of the patient's anatomy can beused to collect parameter measurements. Moreover, the deformedanalytical models can provide a full representation of the impact ofvarious stages of a clinical procedure.

Further improvements to TAVR can be provided by real time comparisons ofvarious simulated parameters of the TAV including a type of TAV, a sizeof TAV, and positioning of the TAV. For example, prior to conductingTAVR, a clinician (e.g., a surgeon) can use the patients anatomicalinformation to simulate various deformed models of the patients anatomy.Accordingly, the sensitivity of the patient to particular positioning ofeach available model of TAV can be evaluated. For example, each model ofTAV can be provided in various positions and the relative amount of riskfor complications such as, coronary obstruction, paravalvular leakage,and thrombosis, can be quantified. Moreover, the deformed models andquantified information can be displayed (e.g., in virtual reality) toallow the clinician to have visual feedback of the results of the TAVRprior to performing the TAVR. Accordingly, the clinical procedure can beperformed with greater control, lower risk, and substantially improvedpatient outcomes.

It is noted that the terms “substantially” and “about” can be usedherein to represent an inherent degree of uncertainty that can beattributed to any quantitative comparison, value, measurement, or otherrepresentation. These terms are also used herein to represent a degreeby which a quantitative representation can vary from a stated referencewithout resulting in a change in the basic function of the subjectmatter at issue.

What have been described above are examples. It is, of course, notpossible to describe every conceivable combination of components ormethods, but one of ordinary skill in the art will recognize that manyfurther combinations and permutations are possible. Accordingly, thedisclosure is intended to embrace all such alterations, modifications,and variations that fall within the scope of this application, includingthe appended claims. Additionally, where the disclosure or claims recite“a,” “an,” “a first,” or “another” element, or the equivalent thereof,it should be interpreted to include one or more than one such element,neither requiring nor excluding two or more such elements. As usedherein, the term “includes” means includes but not limited to, and theterm “including” means including but not limited to. The term “based on”means based at least in part on.

What is claimed is:
 1. A method for predictive heart valve simulation,the method comprising: generating, with an image processing engine,anatomical model data based on image data characterizing anatomicalregions of a heart of a patient, wherein the anatomical model datacomprises three-dimensional shapes of the anatomical regions of theheart; generating, with a geometric modeling engine, analytical modeldata based on the anatomical model data, wherein the analytical modeldata comprises a three-dimensional mesh of the anatomical regions of theheart; generating, with a numerical analysis engine, a deformedanalytical model based on the analytical model data and based athree-dimensional mesh of a surgical object, wherein the deformedanalytical model is indicative of a deformed position of the anatomicalregions of the heart and a deformed position of the surgical object; andevaluating the deformed analytical model to provide heart functionalitymeasures for the heart.
 2. The method of claim 1, wherein the anatomicalregions of the heart comprise an aortic root, a mitral valve, apulmonary root, a pulmonary vein ostium, tricuspid annulus, superiorvena cava ostium, or inferior vena cava ostium.
 3. The method of claim1, wherein the anatomical regions of the heart comprise one or morecalcific nodules, an aortic root comprising a coronary artery, and anaortic leaflet.
 4. The method of claim 3, further comprising: modelingblood flow, with the numerical analysis engine, around the aorticleaflet and the surgical object of the deformed analytical model,wherein the blood flow is indicative of one of a paravalvular leakage,thrombosis, pressure gradient, energy loss, effective orifice area, anda combination thereof.
 5. The method of claim 4, wherein the blood flowcomprises blood flow stasis zones indicative of the thrombosis; and themethod further comprising displaying on a display the blood flow stasiszones and the deformed analytical model.
 6. The method of claim 3,further comprising one of: measuring a gap size between the coronaryartery of the aortic root and the calcific nodule based on the deformedanalytical model; and measuring a gap size between the coronary arteryof the aortic root and the aortic leaflet based on the deformedanalytical model.
 7. The method of claim 3, wherein the heartfunctionality measures comprise heart valve measures comprising one of apressure gradient, an energy loss, a paravalvular leakage, a thrombosisrisk, flow stasis regions, and a combination thereof.
 8. The method ofclaim 1, wherein the surgical object of the deformed analytical modelcomprises a stent; and the method further comprises measuring a gap sizebetween the deformed position of one of the anatomical regions of theheart and the stent, wherein the gap size is indicative of aparavalvular leakage.
 9. The method of claim 1, wherein the surgicalobject of the deformed analytical model comprises an artificial leaflet;and the method further comprises measuring a gap size between thedeformed position of one of the anatomical regions of the heart and theartificial leaflet, wherein the gap size is indicative of a paravalvularleakage.
 10. The method of claim 1, further comprising: repositioningthe surgical object relative to the anatomical regions of the heart togenerate a second anatomical model; generating, with the numericalanalysis engine, a second deformed analytical model based onrepositioned analytical model data, wherein the second deformedanalytical model is indicative of a second deformed position of theanatomical regions of the heart and a second deformed position of thesurgical object; and providing the deformed anatomical model and thesecond deformed anatomical model on a display.
 11. The method of claim10, further comprising segmenting the anatomical regions of the heart ofthe patient from the image data characterizing the heart of the patient.12. The method of claim 1, comprising: replacing the surgical objectwith a second surgical object to generate a second anatomical model;generating, with the numerical analysis engine, a second deformedanalytical model based on repositioned analytical model data, whereinthe second deformed analytical model is indicative of a second deformedposition of the anatomical regions of the heart and a second deformedposition of the surgical object; and providing the deformed anatomicalmodel and the second deformed anatomical model on a display.
 13. Themethod of claim 12, wherein the display comprises a virtual realityheadset.
 14. A method for predictive heart valve simulation, the methodcomprising: segmenting, with one or more processors, anatomical regionsof a heart of a patient from the image data characterizing the heart ofthe patient, wherein the anatomical regions comprise one or morecalcific nodules, an aortic root comprising a coronary artery, and anaortic leaflet; generating, with the one or more processors, anatomicalmodel data based on the image data of the one or more calcific nodules,the aortic root, and the aortic leaflet into, wherein the anatomicalmodel data comprises three-dimensional shapes of the one or morecalcific nodules, the aortic root, and the aortic leaflet; andsimulating, with the one or more processors, a deformed position of theaortic leaflet and the calcific nodule; and quantifying, with the one ormore processors, a gap size based on the deformed position of thecalcific nodule and the coronary artery of the aortic root.
 15. Themethod of claim 14, further comprising normalizing, with the one or moreprocessors, the gap size with a diameter of the coronary artery todetermine a cut-off ratio.
 16. The method of claim 15, wherein, when avalue of the cut-off ratio is less than or equal to 0.5, the cut-offratio is indicative that the patient is at risk for coronaryobstruction.
 17. The method of claim 14, comprising: generating, withthe one or more processors, analytical model data based on theanatomical model data, wherein the analytical model data comprisesthree-dimensional meshes of the one or more calcific nodules, the aorticroot, and the aortic leaflet; and generating, with the one or moreprocessors, a deformed analytical model based on the analytical modeldata, wherein the deformed analytical model is indicative of deformedposition of the calcific nodule and the coronary artery of the aorticroot, and wherein the gap size corresponds to a three-dimensionaldistance between the calcific nodule and the coronary artery of theaortic root.
 18. The method of claim 17, wherein the analytical modeldata comprises a surgical object, and wherein the coronary artery of thedeformed analytical model is deformed in response to the surgicalobject.
 19. The method of claim 18, wherein the surgical object models atranscatheter aortic valve.
 20. The method of claim 14, furthercomprising measuring, with the one or more processors, model parametersbased on the anatomical model data, wherein the gap size is atwo-dimensional distance between a nodule point on the deformed positionof the calcific nodule and an ostium point on the coronary artery of theaortic root.
 21. The method of claim 20, wherein the model parameterscomprise a sinus width w of the coronary artery, a thickness t of thecalcific nodule, a leaflet length l of the aortic leaflet, a height h ofthe coronary artery, and a diameter d of the coronary artery.
 22. Themethod of claim 21, wherein the gap size is determined based on:α_(2D)=√{square root over ((Δx)²+(Δy)²)}, where Δx=w−t and Δy=h+d−l. 23.The method of claim 14, wherein, when a value of the gap size is lessthan or equal to 3 millimeters (mm), the gap size is indicative that thepatient is at risk for coronary obstruction.
 24. A method for predictiveheart valve simulation, the method comprising: segmenting anatomicalregions of a heart of a patient from the image data characterizing theheart of the patient, wherein the anatomical regions comprise one ormore calcific nodules, an aortic root comprising a coronary artery, andan aortic leaflet; generating, with an image processing engine,anatomical model data based on the image data of the one or morecalcific nodules, the aortic root, and the aortic leaflet, wherein theanatomical model data comprises three-dimensional shapes of the one ormore calcific nodules, the aortic root, and the aortic leaflet;generating, with a geometric modeling engine, analytical model databased on the anatomical model data, wherein the analytical model datacomprises three-dimensional meshes of the one or more calcific nodules,the aortic root, and the aortic leaflet; generating, with a numericalanalysis engine, a deformed analytical model based on the analyticalmodel data, wherein the deformed analytical model is indicative of adeformed position of the calcific nodule and the coronary artery of theaortic root; and determining a gap size between the deformed position ofthe calcific nodule and the coronary artery of the aortic root.
 25. Themethod of claim 24, further comprising deforming, with the numericalanalysis engine, the coronary artery of the deformed analytical modelwith a surgical object.
 26. The method of claim 25, wherein the surgicalobject corresponds to a model of a transcatheter aortic valve.
 27. Themethod of claim 24, wherein the image data comprises one of X-ray imagedata, magnetic resonance imaging (MRI) image data, and ultrasound imagedata.
 28. The method of claim 24, further comprising normalizing the gapsize with a diameter of the coronary artery to determine a cut-offratio.
 29. The method of claim 28, wherein, when a value of the cut-offratio is less than or equal to 0.5, the cut-off ratio is indicative thatthe patient is at risk for coronary obstruction.
 30. The method of claim24, wherein, when a value of the gap size is less than or equal to 3millimeters (mm), the gap size is indicative that the patient is at riskfor coronary obstruction.
 31. A method for predictive heart valvesimulation, the method comprising: receiving image data indicative of aheart of a patient, the image data comprising a calcific nodule, anaortic root comprising a coronary artery, and an aortic leaflet;measuring one or more model parameters based on the anatomical modeldata, wherein the one or more model parameters comprise a thickness t ofthe calcific nodule; determining, with a parametric analysis engine, adeformed position of the aortic leaflet and the calcific nodule based onthe one or more model parameters, wherein the parametric analysis engineis programmed to model the aortic leaflet in a fully expanded position;and quantifying, with the parametric analysis engine, a gap size basedon the deformed position of the calcific nodule and the coronary arteryof the aortic root, wherein the gap size corresponds to atwo-dimensional distance between a nodule point on the deformed positionof the calcific nodule and an ostium point on the coronary artery of theaortic root.
 32. The method of claim 31, wherein the model parameterscomprise a sinus width w of the coronary artery, a leaflet length l ofthe aortic leaflet, a height h of the coronary artery 40, and a diameterd of the coronary artery.
 33. The method of claim 32, wherein the gapsize is given by: α_(2D)=√{square root over ((Δx)²+(Δy)²)}, where Δx=w−tand Δy=h+d−l.
 34. The method of claim 31, wherein, when a value of thegap size is less than or equal to 3 millimeters (mm), the gap size isindicative that the patient is at risk for coronary obstruction.
 35. Themethod of claim 31, further comprising normalizing the gap size with adiameter of the coronary artery to determine a cut-off ratio.
 36. Themethod of claim 35, wherein, when a value of the cut-off ratio is lessthan or equal to 0.5, the cut-off ratio is indicative that the patientis at risk for coronary obstruction.